The rise of the Internet has occasioned two disparate phenomena: the increase in the presence of social networks, with their corresponding member profiles visible to large numbers of people, and the increase in the use of these social networks to perform searches for jobs that have been posted on or linked to by the social networks.
A technical problem encountered by social networking services in managing online job searches is that determining how to serve the most appropriate and relevant job results with minimal delay becomes significantly challenging as the number of sources and volume of job opportunities via the social networking services grows at an unprecedented pace.
Personalization of job search results is also preferential. For example, when a user searches for a query like “software engineer,” depending on the skills, background, experience, location, and other factors about the user, the ranked list of results can be drastically different. Thus, for example, a person skilled in machine learning would see a very different set of job results compared to someone specializing in hardware or computer networks.
Historically, algorithms to rank job search results in response to a query have heavily utilized text and entity-based features extracted from the query and job postings to derive a global ranking. However, when such global ranking algorithms are modified to improve certain queries, other queries tend to become degraded. Specifically, the queries that often become degraded are those where personalization is desired, such as in the “software engineer” example provided above. Given the prevalence of such job search queries, it would be beneficial to have a technical solution for providing highly relevant job posting results even if the global ranking model cannot generalize well for these types of queries.